Overview

Dataset statistics

Number of variables14
Number of observations1330
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory292.9 KiB
Average record size in memory225.5 B

Variable types

Categorical4
Numeric10

Alerts

Country Name has a high cardinality: 266 distinct valuesHigh cardinality
Country Code has a high cardinality: 266 distinct valuesHigh cardinality
Current GDP ($) is highly overall correlated with Land area (sq.km) and 1 other fieldsHigh correlation
GDP per capita ($) is highly overall correlated with %individuals using internet and 1 other fieldsHigh correlation
%individuals using internet is highly overall correlated with GDP per capita ($) and 1 other fieldsHigh correlation
Interest payments (% of expense) is highly overall correlated with Interest payments (% of revenue)High correlation
Interest payments (% of revenue) is highly overall correlated with Interest payments (% of expense)High correlation
Land area (sq.km) is highly overall correlated with Current GDP ($) and 1 other fieldsHigh correlation
Life expectancy at birth is highly overall correlated with GDP per capita ($) and 1 other fieldsHigh correlation
Military expenditure (% of GDP) is highly overall correlated with Current GDP ($) and 1 other fieldsHigh correlation
Year is highly overall correlated with AgeHigh correlation
Age is highly overall correlated with YearHigh correlation
Year is uniformly distributedUniform
Country Name is uniformly distributedUniform
Country Code is uniformly distributedUniform
Age is uniformly distributedUniform
Current GDP ($) has 50 (3.8%) zerosZeros
GDP growth (annual %) has 61 (4.6%) zerosZeros
GDP per capita ($) has 50 (3.8%) zerosZeros
Gini index has 825 (62.0%) zerosZeros
%individuals using internet has 75 (5.6%) zerosZeros
Interest payments (% of expense) has 446 (33.5%) zerosZeros
Interest payments (% of revenue) has 441 (33.2%) zerosZeros
Life expectancy at birth has 85 (6.4%) zerosZeros
Military expenditure (% of GDP) has 345 (25.9%) zerosZeros

Reproduction

Analysis started2023-05-22 19:47:15.506171
Analysis finished2023-05-22 19:47:26.166328
Duration10.66 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Year
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size79.4 KiB
2016
266 
2017
266 
2018
266 
2019
266 
2020
266 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5320
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2017
3rd row2018
4th row2019
5th row2020

Common Values

ValueCountFrequency (%)
2016 266
20.0%
2017 266
20.0%
2018 266
20.0%
2019 266
20.0%
2020 266
20.0%

Length

2023-05-22T14:47:26.294886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-22T14:47:26.447249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2016 266
20.0%
2017 266
20.0%
2018 266
20.0%
2019 266
20.0%
2020 266
20.0%

Most occurring characters

ValueCountFrequency (%)
2 1596
30.0%
0 1596
30.0%
1 1064
20.0%
6 266
 
5.0%
7 266
 
5.0%
8 266
 
5.0%
9 266
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5320
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1596
30.0%
0 1596
30.0%
1 1064
20.0%
6 266
 
5.0%
7 266
 
5.0%
8 266
 
5.0%
9 266
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5320
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1596
30.0%
0 1596
30.0%
1 1064
20.0%
6 266
 
5.0%
7 266
 
5.0%
8 266
 
5.0%
9 266
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1596
30.0%
0 1596
30.0%
1 1064
20.0%
6 266
 
5.0%
7 266
 
5.0%
8 266
 
5.0%
9 266
 
5.0%

Country Name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct266
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size90.3 KiB
Afghanistan
 
5
Norway
 
5
Mozambique
 
5
Myanmar
 
5
Namibia
 
5
Other values (261)
1305 

Length

Max length52
Median length44
Mean length12.402256
Min length4

Characters and Unicode

Total characters16495
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan 5
 
0.4%
Norway 5
 
0.4%
Mozambique 5
 
0.4%
Myanmar 5
 
0.4%
Namibia 5
 
0.4%
Nauru 5
 
0.4%
Nepal 5
 
0.4%
Netherlands 5
 
0.4%
New Caledonia 5
 
0.4%
New Zealand 5
 
0.4%
Other values (256) 1280
96.2%

Length

2023-05-22T14:47:26.559793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100
 
4.0%
and 60
 
2.4%
income 55
 
2.2%
ida 50
 
2.0%
africa 45
 
1.8%
islands 45
 
1.8%
asia 40
 
1.6%
ibrd 40
 
1.6%
rep 35
 
1.4%
countries 35
 
1.4%
Other values (310) 2020
80.0%

Most occurring characters

ValueCountFrequency (%)
a 1935
 
11.7%
i 1295
 
7.9%
1195
 
7.2%
e 1150
 
7.0%
n 1110
 
6.7%
r 880
 
5.3%
o 785
 
4.8%
t 650
 
3.9%
s 600
 
3.6%
l 575
 
3.5%
Other values (50) 6320
38.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12475
75.6%
Uppercase Letter 2365
 
14.3%
Space Separator 1195
 
7.2%
Other Punctuation 265
 
1.6%
Open Punctuation 75
 
0.5%
Close Punctuation 75
 
0.5%
Dash Punctuation 45
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1935
15.5%
i 1295
10.4%
e 1150
 
9.2%
n 1110
 
8.9%
r 880
 
7.1%
o 785
 
6.3%
t 650
 
5.2%
s 600
 
4.8%
l 575
 
4.6%
d 565
 
4.5%
Other values (16) 2930
23.5%
Uppercase Letter
ValueCountFrequency (%)
A 260
 
11.0%
S 225
 
9.5%
I 195
 
8.2%
C 180
 
7.6%
B 160
 
6.8%
R 150
 
6.3%
M 145
 
6.1%
D 140
 
5.9%
E 120
 
5.1%
L 105
 
4.4%
Other values (15) 685
29.0%
Other Punctuation
ValueCountFrequency (%)
& 100
37.7%
. 85
32.1%
, 65
24.5%
' 10
 
3.8%
: 5
 
1.9%
Space Separator
ValueCountFrequency (%)
1195
100.0%
Open Punctuation
ValueCountFrequency (%)
( 75
100.0%
Close Punctuation
ValueCountFrequency (%)
) 75
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14840
90.0%
Common 1655
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1935
 
13.0%
i 1295
 
8.7%
e 1150
 
7.7%
n 1110
 
7.5%
r 880
 
5.9%
o 785
 
5.3%
t 650
 
4.4%
s 600
 
4.0%
l 575
 
3.9%
d 565
 
3.8%
Other values (41) 5295
35.7%
Common
ValueCountFrequency (%)
1195
72.2%
& 100
 
6.0%
. 85
 
5.1%
( 75
 
4.5%
) 75
 
4.5%
, 65
 
3.9%
- 45
 
2.7%
' 10
 
0.6%
: 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1935
 
11.7%
i 1295
 
7.9%
1195
 
7.2%
e 1150
 
7.0%
n 1110
 
6.7%
r 880
 
5.3%
o 785
 
4.8%
t 650
 
3.9%
s 600
 
3.6%
l 575
 
3.5%
Other values (50) 6320
38.3%

Country Code
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct266
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
AFG
 
5
NOR
 
5
MOZ
 
5
MMR
 
5
NAM
 
5
Other values (261)
1305 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3990
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFG
2nd rowAFG
3rd rowAFG
4th rowAFG
5th rowAFG

Common Values

ValueCountFrequency (%)
AFG 5
 
0.4%
NOR 5
 
0.4%
MOZ 5
 
0.4%
MMR 5
 
0.4%
NAM 5
 
0.4%
NRU 5
 
0.4%
NPL 5
 
0.4%
NLD 5
 
0.4%
NCL 5
 
0.4%
NZL 5
 
0.4%
Other values (256) 1280
96.2%

Length

2023-05-22T14:47:26.641596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afg 5
 
0.4%
aut 5
 
0.4%
bgr 5
 
0.4%
brn 5
 
0.4%
afw 5
 
0.4%
alb 5
 
0.4%
dza 5
 
0.4%
asm 5
 
0.4%
and 5
 
0.4%
ago 5
 
0.4%
Other values (256) 1280
96.2%

Most occurring characters

ValueCountFrequency (%)
A 325
 
8.1%
S 290
 
7.3%
M 275
 
6.9%
R 260
 
6.5%
N 245
 
6.1%
C 230
 
5.8%
L 220
 
5.5%
E 210
 
5.3%
T 205
 
5.1%
B 200
 
5.0%
Other values (16) 1530
38.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3990
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 325
 
8.1%
S 290
 
7.3%
M 275
 
6.9%
R 260
 
6.5%
N 245
 
6.1%
C 230
 
5.8%
L 220
 
5.5%
E 210
 
5.3%
T 205
 
5.1%
B 200
 
5.0%
Other values (16) 1530
38.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3990
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 325
 
8.1%
S 290
 
7.3%
M 275
 
6.9%
R 260
 
6.5%
N 245
 
6.1%
C 230
 
5.8%
L 220
 
5.5%
E 210
 
5.3%
T 205
 
5.1%
B 200
 
5.0%
Other values (16) 1530
38.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 325
 
8.1%
S 290
 
7.3%
M 275
 
6.9%
R 260
 
6.5%
N 245
 
6.1%
C 230
 
5.8%
L 220
 
5.5%
E 210
 
5.3%
T 205
 
5.1%
B 200
 
5.0%
Other values (16) 1530
38.3%

Current GDP ($)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1272
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5833785 × 1012
Minimum0
Maximum8.7568054 × 1013
Zeros50
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-22T14:47:26.738636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.1697726 × 108
Q17.1841162 × 109
median4.8213165 × 1010
Q35.1339377 × 1011
95-th percentile1.9695651 × 1013
Maximum8.7568054 × 1013
Range8.7568054 × 1013
Interquartile range (IQR)5.0620965 × 1011

Descriptive statistics

Standard deviation8.9314293 × 1012
Coefficient of variation (CV)3.457267
Kurtosis34.591558
Mean2.5833785 × 1012
Median Absolute Deviation (MAD)4.721835 × 1010
Skewness5.3590948
Sum3.4358934 × 1015
Variance7.977043 × 1025
MonotonicityNot monotonic
2023-05-22T14:47:26.827929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
 
3.8%
1.705379935 × 10122
 
0.2%
3.436593923 × 10122
 
0.2%
1.563897898 × 10122
 
0.2%
1.754544849 × 10122
 
0.2%
1.80448233 × 10122
 
0.2%
3.597252024 × 10122
 
0.2%
3.34810885 × 10122
 
0.2%
3.386420412 × 10122
 
0.2%
2.926447614 × 10122
 
0.2%
Other values (1262) 1262
94.9%
ValueCountFrequency (%)
0 50
3.8%
36547799.58 1
 
0.1%
40619251.99 1
 
0.1%
42588164.97 1
 
0.1%
47271463.33 1
 
0.1%
48855550.2 1
 
0.1%
99723394.96 1
 
0.1%
109359680.2 1
 
0.1%
114626625.6 1
 
0.1%
118724073.8 1
 
0.1%
ValueCountFrequency (%)
8.756805441 × 10131
0.1%
8.626760063 × 10131
0.1%
8.474697912 × 10131
0.1%
8.119329166 × 10131
0.1%
7.630505887 × 10131
0.1%
5.50456476 × 10131
0.1%
5.455747758 × 10131
0.1%
5.398312976 × 10131
0.1%
5.346907564 × 10131
0.1%
5.346107754 × 10131
0.1%

GDP growth (annual %)
Real number (ℝ)

Distinct1267
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4858426
Minimum-54.011402
Maximum43.479556
Zeros61
Zeros (%)4.6%
Negative304
Negative (%)22.9%
Memory size10.5 KiB
2023-05-22T14:47:26.932229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-54.011402
5-th percentile-7.1497611
Q10
median2.2846386
Q34.2790871
95-th percentile6.993149
Maximum43.479556
Range97.490958
Interquartile range (IQR)4.2790871

Descriptive statistics

Standard deviation5.2909877
Coefficient of variation (CV)3.5609342
Kurtosis17.401844
Mean1.4858426
Median Absolute Deviation (MAD)2.2153611
Skewness-1.6717473
Sum1976.1706
Variance27.99455
MonotonicityNot monotonic
2023-05-22T14:47:27.030267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61
 
4.6%
6.444305921 2
 
0.2%
4.041559599 2
 
0.2%
0.5 2
 
0.2%
-3.674794578 1
 
0.1%
5.941396863 1
 
0.1%
7.210803008 1
 
0.1%
5.001359945 1
 
0.1%
5.740893151 1
 
0.1%
-1.977329393 1
 
0.1%
Other values (1257) 1257
94.5%
ValueCountFrequency (%)
-54.01140215 1
0.1%
-33.4999021 1
0.1%
-31.30000005 1
0.1%
-26.7828754 1
0.1%
-24.59415687 1
0.1%
-21.46426628 1
0.1%
-20.37364179 1
0.1%
-20.19237057 1
0.1%
-19.34501142 1
0.1%
-18.9795097 1
0.1%
ValueCountFrequency (%)
43.47955594 1
0.1%
29.21212121 1
0.1%
26.68090263 1
0.1%
23.17073171 1
0.1%
19.53581024 1
0.1%
15.13279989 1
0.1%
13.78737302 1
0.1%
13.39624262 1
0.1%
13.39486131 1
0.1%
10.88001864 1
0.1%

GDP per capita ($)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1271
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15856.674
Minimum0
Maximum189487.15
Zeros50
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-22T14:47:27.119862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile463.96171
Q11776.5879
median6070.3686
Q318565.472
95-th percentile61294.604
Maximum189487.15
Range189487.15
Interquartile range (IQR)16788.884

Descriptive statistics

Standard deviation24586.127
Coefficient of variation (CV)1.5505224
Kurtosis14.273292
Mean15856.674
Median Absolute Deviation (MAD)4865.6352
Skewness3.2138681
Sum21089376
Variance6.0447766 × 108
MonotonicityNot monotonic
2023-05-22T14:47:27.219192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
 
3.8%
1652.251801 2
 
0.2%
1501.152921 2
 
0.2%
1529.439256 2
 
0.2%
1589.370013 2
 
0.2%
1630.127378 2
 
0.2%
1627.11036 2
 
0.2%
1823.712912 2
 
0.2%
1959.525845 2
 
0.2%
1894.008884 2
 
0.2%
Other values (1261) 1262
94.9%
ValueCountFrequency (%)
0 50
3.8%
228.2135892 1
 
0.1%
238.783467 1
 
0.1%
238.9907259 1
 
0.1%
253.8263541 1
 
0.1%
260.5652208 1
 
0.1%
315.7779871 1
 
0.1%
363.5695174 1
 
0.1%
384.4634014 1
 
0.1%
389.8220615 1
 
0.1%
ValueCountFrequency (%)
189487.1471 1
0.1%
185978.6093 1
0.1%
180366.7152 1
0.1%
179204.372 1
0.1%
175813.8756 1
0.1%
173688.1894 1
0.1%
171253.9643 1
0.1%
170028.6557 1
0.1%
167517.0597 1
0.1%
165642.3863 1
0.1%

Gini index
Real number (ℝ)

Distinct244
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.88881
Minimum0
Maximum56.3
Zeros825
Zeros (%)62.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-22T14:47:27.325254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q332.4
95-th percentile44.9
Maximum56.3
Range56.3
Interquartile range (IQR)32.4

Descriptive statistics

Standard deviation18.345274
Coefficient of variation (CV)1.3208673
Kurtosis-1.2364628
Mean13.88881
Median Absolute Deviation (MAD)0
Skewness0.69701509
Sum18472.117
Variance336.5491
MonotonicityNot monotonic
2023-05-22T14:47:27.412271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 825
62.0%
36.8 11
 
0.8%
38.5 10
 
0.8%
38.8 7
 
0.5%
35.7 7
 
0.5%
31.4 7
 
0.5%
35.1 7
 
0.5%
31.3 6
 
0.5%
35.3 6
 
0.5%
38 6
 
0.5%
Other values (234) 438
32.9%
ValueCountFrequency (%)
0 825
62.0%
22.8 1
 
0.1%
23.9 1
 
0.1%
24.2 1
 
0.1%
24.4 1
 
0.1%
24.5 1
 
0.1%
24.6 2
 
0.2%
24.8 3
 
0.2%
24.9 2
 
0.2%
25 3
 
0.2%
ValueCountFrequency (%)
56.3 5
0.4%
54.6 5
0.4%
53.9 1
 
0.1%
53.43333333 1
 
0.1%
53.4 1
 
0.1%
53.3 3
0.2%
51.53333333 1
 
0.1%
51.3 6
0.5%
51.2 5
0.4%
50.6 1
 
0.1%

%individuals using internet
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1154
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.836414
Minimum0
Maximum108.05264
Zeros75
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-22T14:47:27.509099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126.409668
median59.37295
Q379.841563
95-th percentile95.957734
Maximum108.05264
Range108.05264
Interquartile range (IQR)53.431895

Descriptive statistics

Standard deviation29.960678
Coefficient of variation (CV)0.55651326
Kurtosis-1.1803436
Mean53.836414
Median Absolute Deviation (MAD)24.876982
Skewness-0.26135658
Sum71602.431
Variance897.64224
MonotonicityNot monotonic
2023-05-22T14:47:27.605806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 75
 
5.6%
4 5
 
0.4%
94.44447158 5
 
0.4%
28 5
 
0.4%
41.77264453 5
 
0.4%
82.0058408 5
 
0.4%
60.18230126 5
 
0.4%
62.38512451 5
 
0.4%
23.62108195 5
 
0.4%
46.88 5
 
0.4%
Other values (1144) 1210
91.0%
ValueCountFrequency (%)
0 75
5.6%
1.17711872 1
 
0.1%
1.30890698 1
 
0.1%
1.44069524 1
 
0.1%
1.5724835 1
 
0.1%
1.70427176 1
 
0.1%
1.88 1
 
0.1%
2.004048698 1
 
0.1%
2.128097396 1
 
0.1%
2.252146094 1
 
0.1%
ValueCountFrequency (%)
108.0526384 1
0.1%
105.0017908 1
0.1%
104.4250923 1
0.1%
103.9047381 1
0.1%
102.5873735 1
0.1%
102.5285135 1
0.1%
102.4520296 1
0.1%
100.999321 1
0.1%
100.7975461 1
0.1%
100.7425746 1
0.1%

Interest payments (% of expense)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct767
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3972341
Minimum-51.923722
Maximum48.670458
Zeros446
Zeros (%)33.5%
Negative6
Negative (%)0.5%
Memory size10.5 KiB
2023-05-22T14:47:27.704211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-51.923722
5-th percentile0
Q10
median3.7012981
Q38.7411145
95-th percentile17.969107
Maximum48.670458
Range100.59418
Interquartile range (IQR)8.7411145

Descriptive statistics

Standard deviation7.0053417
Coefficient of variation (CV)1.2979503
Kurtosis8.1652274
Mean5.3972341
Median Absolute Deviation (MAD)3.7012981
Skewness1.1659065
Sum7178.3214
Variance49.074812
MonotonicityNot monotonic
2023-05-22T14:47:27.793100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 446
33.5%
20.25401766 7
 
0.5%
4.219099179 5
 
0.4%
4.77144465 5
 
0.4%
22.52979905 5
 
0.4%
10.63303885 5
 
0.4%
7.885434605 4
 
0.3%
8.325766621 4
 
0.3%
8.627902592 3
 
0.2%
9.977814629 3
 
0.2%
Other values (757) 843
63.4%
ValueCountFrequency (%)
-51.92372234 1
 
0.1%
-32.53604804 1
 
0.1%
-13.14837374 1
 
0.1%
-9.751464892 1
 
0.1%
-4.875732446 1
 
0.1%
-0.882937069 1
 
0.1%
0 446
33.5%
5.202715133 × 10-51
 
0.1%
0.0007913156893 1
 
0.1%
0.001032311345 1
 
0.1%
ValueCountFrequency (%)
48.67045838 1
0.1%
41.99611574 1
0.1%
39.05322203 1
0.1%
37.25543899 1
0.1%
36.31731741 1
0.1%
36.20937236 1
0.1%
36.05427344 1
0.1%
35.55258394 1
0.1%
34.46720087 1
0.1%
33.93817668 1
0.1%

Interest payments (% of revenue)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct763
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4039723
Minimum-43.598854
Maximum51.265706
Zeros441
Zeros (%)33.2%
Negative6
Negative (%)0.5%
Memory size10.5 KiB
2023-05-22T14:47:27.890066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-43.598854
5-th percentile0
Q10
median3.4773545
Q38.3945608
95-th percentile18.459082
Maximum51.265706
Range94.864561
Interquartile range (IQR)8.3945608

Descriptive statistics

Standard deviation7.5173838
Coefficient of variation (CV)1.3910848
Kurtosis10.523928
Mean5.4039723
Median Absolute Deviation (MAD)3.4773545
Skewness2.1310418
Sum7187.2832
Variance56.511059
MonotonicityNot monotonic
2023-05-22T14:47:27.981574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 441
33.2%
18.45908157 7
 
0.5%
5.128515312 6
 
0.5%
25.63307435 5
 
0.4%
10.32094364 5
 
0.4%
4.138206671 5
 
0.4%
8.449908961 4
 
0.3%
8.573623071 4
 
0.3%
7.964325512 4
 
0.3%
3.697633327 3
 
0.2%
Other values (753) 846
63.6%
ValueCountFrequency (%)
-43.5988544 1
 
0.1%
-27.43784384 1
 
0.1%
-11.27683328 1
 
0.1%
-8.627802983 1
 
0.1%
-4.313901491 1
 
0.1%
-0.8527995823 1
 
0.1%
0 441
33.2%
4.774931826 × 10-51
 
0.1%
0.0006391622917 1
 
0.1%
0.0007314819854 1
 
0.1%
ValueCountFrequency (%)
51.26570614 1
0.1%
50.99192626 1
0.1%
50.83547036 1
0.1%
50.82533241 1
0.1%
50.8219531 1
0.1%
49.97975718 1
0.1%
47.46936269 1
0.1%
45.69641563 1
0.1%
44.45389651 1
0.1%
44.09873289 1
0.1%

Land area (sq.km)
Real number (ℝ)

Distinct386
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5147168.9
Minimum0
Maximum1.2995663 × 108
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-22T14:47:28.069071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile180
Q118280
median192165
Q31246700
95-th percentile24995526
Maximum1.2995663 × 108
Range1.2995663 × 108
Interquartile range (IQR)1228420

Descriptive statistics

Standard deviation15006758
Coefficient of variation (CV)2.9155363
Kurtosis29.413577
Mean5147168.9
Median Absolute Deviation (MAD)191723
Skewness4.9465805
Sum6.8457346 × 109
Variance2.2520278 × 1014
MonotonicityNot monotonic
2023-05-22T14:47:28.174367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
460 15
 
1.1%
180 10
 
0.8%
4770754 6
 
0.5%
23852823.23 6
 
0.5%
96320 5
 
0.4%
1759540 5
 
0.4%
160 5
 
0.4%
2430 5
 
0.4%
1943950 5
 
0.4%
94280 5
 
0.4%
Other values (376) 1263
95.0%
ValueCountFrequency (%)
0 5
0.4%
2.027 5
0.4%
10 5
0.4%
20 5
0.4%
30 5
0.4%
30.5 1
 
0.1%
30.8 1
 
0.1%
32.9 3
0.2%
34 5
0.4%
50 5
0.4%
ValueCountFrequency (%)
129956634.1 1
 
0.1%
129956069.1 1
 
0.1%
129949282.7 3
0.2%
96071736.24 1
 
0.1%
96071433.23 1
 
0.1%
96060596.43 3
0.2%
94132254.24 1
 
0.1%
94131711.23 1
 
0.1%
94120894.43 3
0.2%
79178654.9 1
 
0.1%

Life expectancy at birth
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1217
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.818532
Minimum0
Maximum85.361789
Zeros85
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-22T14:47:28.274004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q165.045851
median73.106833
Q377.4175
95-th percentile82.605366
Maximum85.361789
Range85.361789
Interquartile range (IQR)12.371649

Descriptive statistics

Standard deviation19.094872
Coefficient of variation (CV)0.28155833
Kurtosis7.2931696
Mean67.818532
Median Absolute Deviation (MAD)5.5027642
Skewness-2.7843491
Sum90198.648
Variance364.61415
MonotonicityNot monotonic
2023-05-22T14:47:28.368690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85
 
6.4%
78.29268293 5
 
0.4%
82.29512195 2
 
0.2%
82.6 2
 
0.2%
69.88610387 2
 
0.2%
74.64 2
 
0.2%
69.64361743 2
 
0.2%
69.41226994 2
 
0.2%
69.17132773 2
 
0.2%
68.9161581 2
 
0.2%
Other values (1207) 1224
92.0%
ValueCountFrequency (%)
0 85
6.4%
51.593 1
 
0.1%
52.059 1
 
0.1%
52.24 1
 
0.1%
52.805 1
 
0.1%
52.947 1
 
0.1%
53.283 1
 
0.1%
53.438 1
 
0.1%
53.444 1
 
0.1%
53.541 1
 
0.1%
ValueCountFrequency (%)
85.36178862 1
0.1%
85.07804878 1
0.1%
84.93414634 1
0.1%
84.6804878 1
0.1%
84.4801626 1
0.1%
84.374 1
0.1%
84.35634146 1
0.1%
84.244 1
0.1%
84.22682927 1
0.1%
84.21097561 1
0.1%

Military expenditure (% of GDP)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct960
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.43032
Minimum0
Maximum12.119548
Zeros345
Zeros (%)25.9%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2023-05-22T14:47:28.604676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.2391948
Q31.9543606
95-th percentile4.0656366
Maximum12.119548
Range12.119548
Interquartile range (IQR)1.9543606

Descriptive statistics

Standard deviation1.4720676
Coefficient of variation (CV)1.0291876
Kurtosis8.1780757
Mean1.43032
Median Absolute Deviation (MAD)0.8370998
Skewness2.1390226
Sum1902.3256
Variance2.1669832
MonotonicityNot monotonic
2023-05-22T14:47:28.701917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 345
 
25.9%
3.555482552 5
 
0.4%
3.252009982 2
 
0.2%
1.663095432 2
 
0.2%
2.962890662 2
 
0.2%
2.525700693 2
 
0.2%
2.521452114 2
 
0.2%
2.464627039 2
 
0.2%
2.513960695 2
 
0.2%
2.773525237 2
 
0.2%
Other values (950) 964
72.5%
ValueCountFrequency (%)
0 345
25.9%
0.000846300591 1
 
0.1%
0.0008810297312 1
 
0.1%
0.001394212584 1
 
0.1%
0.001500801641 1
 
0.1%
0.002894074715 1
 
0.1%
0.155938299 1
 
0.1%
0.159564903 1
 
0.1%
0.1624918183 1
 
0.1%
0.1710407458 1
 
0.1%
ValueCountFrequency (%)
12.11954816 1
0.1%
10.87089296 1
0.1%
10.22384814 1
0.1%
9.87273833 1
0.1%
9.635787938 1
0.1%
9.54295828 1
0.1%
9.469201025 1
0.1%
8.637691241 1
0.1%
8.447598946 1
0.1%
7.812684846 1
0.1%

Age
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size75.5 KiB
0
266 
1
266 
2
266 
3
266 
4
266 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1330
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
0 266
20.0%
1 266
20.0%
2 266
20.0%
3 266
20.0%
4 266
20.0%

Length

2023-05-22T14:47:28.790935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-22T14:47:28.872257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 266
20.0%
1 266
20.0%
2 266
20.0%
3 266
20.0%
4 266
20.0%

Most occurring characters

ValueCountFrequency (%)
0 266
20.0%
1 266
20.0%
2 266
20.0%
3 266
20.0%
4 266
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1330
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 266
20.0%
1 266
20.0%
2 266
20.0%
3 266
20.0%
4 266
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 266
20.0%
1 266
20.0%
2 266
20.0%
3 266
20.0%
4 266
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 266
20.0%
1 266
20.0%
2 266
20.0%
3 266
20.0%
4 266
20.0%

Interactions

2023-05-22T14:47:24.518530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:16.254596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:17.175612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:18.012467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:18.985117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:19.894127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:20.756498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:21.660527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:22.586756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:23.498459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:24.626031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:16.343690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:17.256209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:18.092613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:19.121293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:19.974425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:20.838109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:21.741226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:22.674791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:23.577364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:24.888584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:16.455811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:17.336157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:18.172434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:19.201854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:20.062918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:20.926757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:21.831016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:22.771627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:23.665768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:25.008885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:16.564520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:17.424706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:18.356775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:19.298131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:20.154357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:21.014891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:21.927098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:22.884070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:23.809946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:25.113211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:16.657629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:17.505183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:18.445280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:19.386310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:20.231893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:21.097797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:22.008379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:22.964430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:23.914338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:25.225404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:16.762189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:17.593923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:18.534031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:19.474734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:20.314928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:21.185210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:22.114865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:23.053993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:24.011417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:25.329945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:16.842681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:17.674611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:18.623500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:19.555266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:20.402113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:21.265826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:22.225201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:23.141431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:24.115494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:25.442643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:16.922932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:17.748358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:18.710757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:19.635814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:20.489434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:21.397900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:22.315099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:23.231889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:24.213011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:25.555565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:17.005786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:17.835171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:18.799236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:19.724214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:20.578705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:21.491760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:22.410486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:23.319086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:24.309564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:25.652048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:17.086960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:17.915432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:18.888621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:19.805428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:20.660220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:21.572230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:22.498635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:23.407380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T14:47:24.414350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-22T14:47:28.953118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Current GDP ($)GDP growth (annual %)GDP per capita ($)Gini index%individuals using internetInterest payments (% of expense)Interest payments (% of revenue)Land area (sq.km)Life expectancy at birthMilitary expenditure (% of GDP)YearAge
Current GDP ($)1.0000.0610.2830.0850.2860.3930.4240.7610.3750.5080.0000.000
GDP growth (annual %)0.0611.000-0.1660.075-0.2120.0730.0720.112-0.1390.0340.2440.244
GDP per capita ($)0.283-0.1661.0000.0920.770-0.041-0.010-0.1950.658-0.0620.0000.000
Gini index0.0850.0750.0921.0000.1420.1670.191-0.0330.2350.0900.0000.000
%individuals using internet0.286-0.2120.7700.1421.000-0.034-0.005-0.1660.6950.0570.0910.091
Interest payments (% of expense)0.3930.073-0.0410.167-0.0341.0000.9920.3590.0340.2220.0000.000
Interest payments (% of revenue)0.4240.072-0.0100.191-0.0050.9921.0000.3710.0670.2330.0000.000
Land area (sq.km)0.7610.112-0.195-0.033-0.1660.3590.3711.000-0.0560.5290.0000.000
Life expectancy at birth0.375-0.1390.6580.2350.6950.0340.067-0.0561.0000.1280.0000.000
Military expenditure (% of GDP)0.5080.034-0.0620.0900.0570.2220.2330.5290.1281.0000.0000.000
Year0.0000.2440.0000.0000.0910.0000.0000.0000.0000.0001.0001.000
Age0.0000.2440.0000.0000.0910.0000.0000.0000.0000.0001.0001.000

Missing values

2023-05-22T14:47:25.820967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-22T14:47:26.054228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

YearCountry NameCountry CodeCurrent GDP ($)GDP growth (annual %)GDP per capita ($)Gini index%individuals using internetInterest payments (% of expense)Interest payments (% of revenue)Land area (sq.km)Life expectancy at birthMilitary expenditure (% of GDP)Age
02016AfghanistanAFG1.811656e+102.260314512.0127780.00.0000000.1080670.0915446.528600e+0563.7630000.9567720
12017AfghanistanAFG1.875347e+102.647003516.6798620.00.0000000.1342070.1093796.528600e+0564.1300000.9452271
22018AfghanistanAFG1.805323e+101.189228485.6684190.00.0000000.1603470.1272146.528600e+0564.4860001.0067462
32019AfghanistanAFG1.879945e+103.911603494.1793500.00.0000000.1864870.1450506.528600e+0564.8330001.1182313
42020AfghanistanAFG2.011614e+10-2.351101516.7478710.00.0000000.2126270.1628856.528600e+0565.1896671.3696844
52016Africa Eastern and SouthernAFE8.733549e+112.0193911431.7787230.017.1655879.8072549.1802431.480707e+0762.7876811.4204300
62017Africa Eastern and SouthernAFE9.853557e+112.5422981573.0633860.020.3321198.6279038.3041321.480704e+0763.2462641.3766361
72018Africa Eastern and SouthernAFE1.012853e+122.4752721574.9786480.023.6573669.99305010.3457491.480704e+0763.6489881.1644072
82019Africa Eastern and SouthernAFE1.009910e+122.0778981530.0591770.026.44828410.63303910.3209441.480704e+0764.0051971.1160593
92020Africa Eastern and SouthernAFE9.207923e+11-2.9391861359.6182240.029.54251610.90830010.7011771.480704e+0764.4110361.1514574
YearCountry NameCountry CodeCurrent GDP ($)GDP growth (annual %)GDP per capita ($)Gini index%individuals using internetInterest payments (% of expense)Interest payments (% of revenue)Land area (sq.km)Life expectancy at birthMilitary expenditure (% of GDP)Age
13202016ZambiaZMB2.095841e+103.7766791280.8065430.04.89999121.97308824.839807743390.062.4640001.4293050
13212017ZambiaZMB2.587360e+103.5043361535.1965740.09.59999422.83194524.149885743390.063.0430001.3095961
13222018ZambiaZMB2.631159e+104.0344941516.3683710.014.29999733.71811033.207297743390.063.5100001.4094622
13232019ZambiaZMB2.330867e+101.4413061305.0010310.019.00000041.99611644.453897743390.063.8860001.2193753
13242020ZambiaZMB1.811063e+10-2.785055985.1324360.023.70000348.67045850.991926743390.064.3600001.1542254
13252016ZimbabweZWE2.054868e+100.7558691464.58895741.323.1199892.7479534.033116386850.060.2940001.7424940
13262017ZimbabweZWE1.758489e+104.7094921235.18903244.324.4000003.6693406.024985386850.060.8120001.5449481
13272018ZimbabweZWE1.811554e+104.8242111254.64226547.325.0000009.96258013.473282386850.061.1950001.2227952
13282019ZimbabweZWE1.928429e+10-6.1442361316.74065750.325.10000013.56989318.193365386850.061.4900000.6986013
13292020ZimbabweZWE1.805117e+10-6.2487481214.50982053.325.76000417.17720622.913448386850.061.8886670.3506374